




'''simple docstring'''


import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)





class  __lowercase    (	__magic_name__			):







   def __init__(						self   ,				UpperCamelCase=0.01   ,				UpperCamelCase=1000   )					->     Union[str, Any]:
     __a								=       p_stop
     __a								=       max_length







   def __iter__(						self   )					->     Any:
     __a								=       0
     __a								=       False
     while not stop and count < self.max_length:
       yield count
       count += 1
       __a								=       random.random() < self.p_stop





class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase=False   ,				UpperCamelCase=True   )					->     int:
     __a								=       [
         BatchSamplerShard(UpperCamelCase   ,				2   ,				UpperCamelCase   ,				split_batches=UpperCamelCase   ,				even_batches=UpperCamelCase   )
         for i in range(2   )
     ]
     __a								=       [list(UpperCamelCase   ) for batch_sampler_shard in batch_sampler_shards]
     if not split_batches:
       self.assertListEqual([len(UpperCamelCase   ) for shard in batch_sampler_shards]   ,				[len(UpperCamelCase   ) for e in expected]   )
     self.assertListEqual(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Dict:
     # Check the shards when the dataset is a round multiple of total batch size.
     __a								=       BatchSampler(range(24   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
         [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   )

     __a								=       BatchSampler(range(24   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     # Expected shouldn't change
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   )

     # Check the shards when the dataset is a round multiple of batch size but not total batch size.
     __a								=       BatchSampler(range(21   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
         [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   )

     __a								=       BatchSampler(range(21   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
         [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   )

     # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
     # num_processes batch.
     __a								=       BatchSampler(range(22   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
         [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   )

     __a								=       BatchSampler(range(22   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
         [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   )

     # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
     # num_processes batch.
     __a								=       BatchSampler(range(20   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
         [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   )

     __a								=       BatchSampler(range(20   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
         [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   )

     # Check the shards when the dataset is very small.
     __a								=       BatchSampler(range(2   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     __a								=       [[[0, 1, 0]], [[1, 0, 1]]]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   )

     __a								=       BatchSampler(range(2   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     __a								=       [[], []]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     int:
     # Check the shards when the dataset is a round multiple of batch size.
     __a								=       BatchSampler(range(24   )   ,				batch_size=4   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
         [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				split_batches=UpperCamelCase   )

     __a								=       BatchSampler(range(24   )   ,				batch_size=4   ,				drop_last=UpperCamelCase   )
     # Expected shouldn't change
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				split_batches=UpperCamelCase   )

     # Check the shards when the dataset is not a round multiple of batch size.
     __a								=       BatchSampler(range(22   )   ,				batch_size=4   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
         [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				split_batches=UpperCamelCase   )

     __a								=       BatchSampler(range(22   )   ,				batch_size=4   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
         [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				split_batches=UpperCamelCase   )

     # Check the shards when the dataset is not a round multiple of batch size or num_processes.
     __a								=       BatchSampler(range(21   )   ,				batch_size=4   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
         [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				split_batches=UpperCamelCase   )

     __a								=       BatchSampler(range(21   )   ,				batch_size=4   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
         [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				split_batches=UpperCamelCase   )

     # Check the shards when the dataset is very small.
     __a								=       BatchSampler(range(2   )   ,				batch_size=4   ,				drop_last=UpperCamelCase   )
     __a								=       [[[0, 1]], [[0, 1]]]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				split_batches=UpperCamelCase   )

     __a								=       BatchSampler(range(2   )   ,				batch_size=4   ,				drop_last=UpperCamelCase   )
     __a								=       [[], []]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				split_batches=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     # Check the shards when the dataset is a round multiple of total batch size.
     __a								=       BatchSampler(range(24   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
         [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				even_batches=UpperCamelCase   )

     __a								=       BatchSampler(range(24   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     # Expected shouldn't change
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				even_batches=UpperCamelCase   )

     # Check the shards when the dataset is a round multiple of batch size but not total batch size.
     __a								=       BatchSampler(range(21   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
         [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				even_batches=UpperCamelCase   )

     __a								=       BatchSampler(range(21   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
         [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				even_batches=UpperCamelCase   )

     # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
     # num_processes batch.
     __a								=       BatchSampler(range(22   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
         [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				even_batches=UpperCamelCase   )

     __a								=       BatchSampler(range(22   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
         [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				even_batches=UpperCamelCase   )

     # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
     # num_processes batch.
     __a								=       BatchSampler(range(20   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
         [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				even_batches=UpperCamelCase   )

     __a								=       BatchSampler(range(20   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
         [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				even_batches=UpperCamelCase   )

     # Check the shards when the dataset is very small.
     __a								=       BatchSampler(range(2   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     __a								=       [[[0, 1]], []]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				even_batches=UpperCamelCase   )

     __a								=       BatchSampler(range(2   )   ,				batch_size=3   ,				drop_last=UpperCamelCase   )
     __a								=       [[], []]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				even_batches=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     # Check the shards when the dataset is a round multiple of batch size.
     __a								=       BatchSampler(range(24   )   ,				batch_size=4   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
         [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				split_batches=UpperCamelCase   ,				even_batches=UpperCamelCase   )

     __a								=       BatchSampler(range(24   )   ,				batch_size=4   ,				drop_last=UpperCamelCase   )
     # Expected shouldn't change
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				split_batches=UpperCamelCase   ,				even_batches=UpperCamelCase   )

     # Check the shards when the dataset is not a round multiple of batch size.
     __a								=       BatchSampler(range(22   )   ,				batch_size=4   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
         [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				split_batches=UpperCamelCase   ,				even_batches=UpperCamelCase   )

     __a								=       BatchSampler(range(22   )   ,				batch_size=4   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
         [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				split_batches=UpperCamelCase   ,				even_batches=UpperCamelCase   )

     # Check the shards when the dataset is not a round multiple of batch size or num_processes.
     __a								=       BatchSampler(range(21   )   ,				batch_size=4   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
         [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				split_batches=UpperCamelCase   ,				even_batches=UpperCamelCase   )

     __a								=       BatchSampler(range(21   )   ,				batch_size=4   ,				drop_last=UpperCamelCase   )
     __a								=       [
         [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
         [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
     ]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				split_batches=UpperCamelCase   ,				even_batches=UpperCamelCase   )

     # Check the shards when the dataset is very small.
     __a								=       BatchSampler(range(2   )   ,				batch_size=4   ,				drop_last=UpperCamelCase   )
     __a								=       [[[0, 1]], []]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				split_batches=UpperCamelCase   ,				even_batches=UpperCamelCase   )

     __a								=       BatchSampler(range(2   )   ,				batch_size=4   ,				drop_last=UpperCamelCase   )
     __a								=       [[], []]
     self.check_batch_sampler_shards(UpperCamelCase   ,				UpperCamelCase   ,				split_batches=UpperCamelCase   ,				even_batches=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a								=       [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
     __a								=       [BatchSamplerShard(UpperCamelCase   ,				2   ,				UpperCamelCase   ,				even_batches=UpperCamelCase   ) for i in range(2   )]

     self.assertEqual(len(batch_sampler_shards[0]   )   ,				3   )
     self.assertEqual(len(batch_sampler_shards[1]   )   ,				2   )

     self.assertListEqual(list(batch_sampler_shards[0]   )   ,				[[0, 1, 2], [5, 6, 7, 8], [12, 13]]   )
     self.assertListEqual(list(batch_sampler_shards[1]   )   ,				[[3, 4], [9, 10, 11]]   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase=False   ,				UpperCamelCase=2   ,				UpperCamelCase=False   )					->     Optional[int]:
     random.seed(UpperCamelCase   )
     __a								=       list(UpperCamelCase   )

     __a								=       [
         IterableDatasetShard(
             UpperCamelCase   ,				batch_size=UpperCamelCase   ,				drop_last=UpperCamelCase   ,				num_processes=UpperCamelCase   ,				process_index=UpperCamelCase   ,				split_batches=UpperCamelCase   ,				)
         for i in range(UpperCamelCase   )
     ]
     __a								=       []
     for iterable_dataset_shard in iterable_dataset_shards:
       # Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
       random.seed(UpperCamelCase   )
       iterable_dataset_lists.append(list(UpperCamelCase   )   )

     __a								=       batch_size // num_processes if split_batches else batch_size
     # All iterable dataset shard should have the same length, a round multiple of shard_batch_size
     __a								=       iterable_dataset_lists[0]
     for l in iterable_dataset_lists[1:]:
       self.assertEqual(len(UpperCamelCase   )   ,				len(UpperCamelCase   )   )
       self.assertTrue(len(UpperCamelCase   ) % shard_batch_size == 0   )

     __a								=       []
     for idx in range(0   ,				len(UpperCamelCase   )   ,				UpperCamelCase   ):
       for l in iterable_dataset_lists:
         observed += l[idx : idx + shard_batch_size]

     if not drop_last:
       while len(UpperCamelCase   ) < len(UpperCamelCase   ):
         reference += reference
     self.assertListEqual(UpperCamelCase   ,				reference[: len(UpperCamelCase   )]   )







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       42
     __a								=       RandomIterableDataset()

     self.check_iterable_dataset_shards(UpperCamelCase   ,				UpperCamelCase   ,				batch_size=4   ,				drop_last=UpperCamelCase   ,				split_batches=UpperCamelCase   )
     self.check_iterable_dataset_shards(UpperCamelCase   ,				UpperCamelCase   ,				batch_size=4   ,				drop_last=UpperCamelCase   ,				split_batches=UpperCamelCase   )
     self.check_iterable_dataset_shards(UpperCamelCase   ,				UpperCamelCase   ,				batch_size=4   ,				drop_last=UpperCamelCase   ,				split_batches=UpperCamelCase   )
     self.check_iterable_dataset_shards(UpperCamelCase   ,				UpperCamelCase   ,				batch_size=4   ,				drop_last=UpperCamelCase   ,				split_batches=UpperCamelCase   )

     # Edge case with a very small dataset
     __a								=       RandomIterableDataset(max_length=2   )

     self.check_iterable_dataset_shards(UpperCamelCase   ,				UpperCamelCase   ,				batch_size=4   ,				drop_last=UpperCamelCase   ,				split_batches=UpperCamelCase   )
     self.check_iterable_dataset_shards(UpperCamelCase   ,				UpperCamelCase   ,				batch_size=4   ,				drop_last=UpperCamelCase   ,				split_batches=UpperCamelCase   )
     self.check_iterable_dataset_shards(UpperCamelCase   ,				UpperCamelCase   ,				batch_size=4   ,				drop_last=UpperCamelCase   ,				split_batches=UpperCamelCase   )
     self.check_iterable_dataset_shards(UpperCamelCase   ,				UpperCamelCase   ,				batch_size=4   ,				drop_last=UpperCamelCase   ,				split_batches=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     __a								=       BatchSampler(range(16   )   ,				batch_size=4   ,				drop_last=UpperCamelCase   )
     __a								=       SkipBatchSampler(UpperCamelCase   ,				2   )
     self.assertListEqual(list(UpperCamelCase   )   ,				[[8, 9, 10, 11], [12, 13, 14, 15]]   )







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     __a								=       SkipDataLoader(list(range(16   )   )   ,				batch_size=4   ,				skip_batches=2   )
     self.assertListEqual([t.tolist() for t in dataloader]   ,				[[8, 9, 10, 11], [12, 13, 14, 15]]   )







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     __a								=       DataLoader(list(range(16   )   )   ,				batch_size=4   )
     __a								=       skip_first_batches(UpperCamelCase   ,				num_batches=2   )
     self.assertListEqual([t.tolist() for t in new_dataloader]   ,				[[8, 9, 10, 11], [12, 13, 14, 15]]   )







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a								=       DataLoaderShard(list(range(16   )   )   ,				batch_size=4   )
     for idx, _ in enumerate(UpperCamelCase   ):
       self.assertEqual(dataloader.end_of_dataloader   ,				idx == 3   )

     # Test it also works on the second iteration
     for idx, _ in enumerate(UpperCamelCase   ):
       self.assertEqual(dataloader.end_of_dataloader   ,				idx == 3   )







   def 		UpperCamelCase__      (						self   )					->     Dict:
     Accelerator()
     __a								=       DataLoaderDispatcher(range(16   )   ,				batch_size=4   )
     for idx, _ in enumerate(UpperCamelCase   ):
       self.assertEqual(dataloader.end_of_dataloader   ,				idx == 3   )

     # Test it also works on the second iteration
     for idx, _ in enumerate(UpperCamelCase   ):
       self.assertEqual(dataloader.end_of_dataloader   ,				idx == 3   )









'''simple docstring'''


from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
UpperCAmelCase_					=   Lock()


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				Tuple					,       a_						:				Dict					,       a_						:				Optional[Any]					,       a_						:				str					,       a_						:				Any					,       a_						:				Optional[Any]      ):
  global process_lock

  # we perform n swaps since after n swaps we know we are sorted
  # we *could* stop early if we are sorted already, but it takes as long to
  # find out we are sorted as it does to sort the list with this algorithm
  for i in range(0					,       10      ):
    if (i + position) % 2 == 0 and r_send is not None:
      # send your value to your right neighbor
      process_lock.acquire()
      r_send[1].send(a_      )
      process_lock.release()

      # receive your right neighbor's value
      process_lock.acquire()
      __a								=       rr_cv[0].recv()
      process_lock.release()

      # take the lower value since you are on the left
      __a								=       min(a_					,       a_      )
    elif (i + position) % 2 != 0 and l_send is not None:
      # send your value to your left neighbor
      process_lock.acquire()
      l_send[1].send(a_      )
      process_lock.release()

      # receive your left neighbor's value
      process_lock.acquire()
      __a								=       lr_cv[0].recv()
      process_lock.release()

      # take the higher value since you are on the right
      __a								=       max(a_					,       a_      )
    # after all swaps are performed, send the values back to main
  result_pipe[1].send(a_      )


def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]      ):
  __a								=       []
  __a								=       []
  # initialize the list of pipes where the values will be retrieved
  for _ in arr:
    result_pipe.append(Pipe()      )
  # creates the processes
  # the first and last process only have one neighbor so they are made outside
  # of the loop
  __a								=       Pipe()
  __a								=       Pipe()
  process_array_.append(
      Process(
          target=a_					,       args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])					,       )      )
  __a								=       temp_rs
  __a								=       temp_rr

  for i in range(1					,       len(a_      ) - 1      ):
    __a								=       Pipe()
    __a								=       Pipe()
    process_array_.append(
        Process(
            target=a_					,       args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])					,       )      )
    __a								=       temp_rs
    __a								=       temp_rr

  process_array_.append(
      Process(
          target=a_					,       args=(
              len(a_      ) - 1,
              arr[len(a_      ) - 1],
              temp_ls,
              None,
              temp_lr,
              None,
              result_pipe[len(a_      ) - 1],
          )					,       )      )

  # start the processes
  for p in process_array_:
    p.start()

  # wait for the processes to end and write their values to the list
  for p in range(0					,       len(a_      )      ):
    __a								=       result_pipe[p][0].recv()
    process_array_[p].join()
  return arr


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       list(range(10					,       0					,       -1      )      )
  print('Initial List'      )
  print(*a_      )
  __a								=       odd_even_transposition(a_      )
  print('Sorted List\n'      )
  print(*a_      )


if __name__ == "__main__":
  main()



